101,065 research outputs found
API design for machine learning software: experiences from the scikit-learn project
Scikit-learn is an increasingly popular machine learning li- brary. Written
in Python, it is designed to be simple and efficient, accessible to
non-experts, and reusable in various contexts. In this paper, we present and
discuss our design choices for the application programming interface (API) of
the project. In particular, we describe the simple and elegant interface shared
by all learning and processing units in the library and then discuss its
advantages in terms of composition and reusability. The paper also comments on
implementation details specific to the Python ecosystem and analyzes obstacles
faced by users and developers of the library
Machine Learning Techniques for Stellar Light Curve Classification
We apply machine learning techniques in an attempt to predict and classify
stellar properties from noisy and sparse time series data. We preprocessed over
94 GB of Kepler light curves from MAST to classify according to ten distinct
physical properties using both representation learning and feature engineering
approaches. Studies using machine learning in the field have been primarily
done on simulated data, making our study one of the first to use real light
curve data for machine learning approaches. We tuned our data using previous
work with simulated data as a template and achieved mixed results between the
two approaches. Representation learning using a Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN) produced no successful predictions, but our work
with feature engineering was successful for both classification and regression.
In particular, we were able to achieve values for stellar density, stellar
radius, and effective temperature with low error (~ 2 - 4%) and good accuracy
(~ 75%) for classifying the number of transits for a given star. The results
show promise for improvement for both approaches upon using larger datasets
with a larger minority class. This work has the potential to provide a
foundation for future tools and techniques to aid in the analysis of
astrophysical data.Comment: Accepted to The Astronomical Journa
Comparative Evaluation of Packet Classification Algorithms for Implementation on Resource Constrained Systems
This paper provides a comparative evaluation of a number of known classification algorithms that have been considered for both software and hardware implementation. Differently from other sources, the comparison has been carried out on implementations based on the same principles and design choices. Performance measurements are obtained by feeding the implemented classifiers with various traffic traces in the same test scenario. The comparison also takes into account implementation feasibility of the considered algorithms in resource constrained systems (e.g. embedded processors on special purpose network platforms). In particular, the comparison focuses on achieving a good compromise between performance, memory usage, flexibility and code portability to different target platforms
The applications of deep neural networks to sdBV classification
With several new large-scale surveys on the horizon, including LSST, TESS,
ZTF, and Evryscope, faster and more accurate analysis methods will be required
to adequately process the enormous amount of data produced. Deep learning, used
in industry for years now, allows for advanced feature detection in minimally
prepared datasets at very high speeds; however, despite the advantages of this
method, its application to astrophysics has not yet been extensively explored.
This dearth may be due to a lack of training data available to researchers.
Here we generate synthetic data loosely mimicking the properties of acoustic
mode pulsating stars and we show that two separate paradigms of deep learning -
the Artificial Neural Network And the Convolutional Neural Network - can both
be used to classify this synthetic data effectively. And that additionally this
classification can be performed at relatively high levels of accuracy with
minimal time spent adjusting network hyperparameters.Comment: 12 pages, 10 figures, originally presented at sdOB
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